No News in Business Cycles
Ma io Fo ni∗
Uni e si `a di Modena e Reggio Emilia
CEPR and RECen
Luca Gambe i†
Uni e si a Au onoma de Ba celona
Luca Sala‡
Uni e si `a Bocconi and IGIER
Feb ua y 21, 2011
Abs ac
This pape uses a s uc u al, la ge dimensional ac o model o e alua e he ole o ‘news’
shocks (shocks wi h a delayed e ec on p oduc i i y) in gene a ing he business cycle.
We ind ha (i) exis ing small-scale VECM models a e a ec ed by ‘non- undamen alness’
and he e o e ail o eco e he co ec shock and impulse esponse unc ions; (ii) news
shocks ha e a limi ed ole in explaining he business cycle; (iii) hei e ec s a e in line
wi h wha p edic ed by s anda d neoclassical heo y; (i ) he bulk o business cycle luc-
ua ions a e explained by shocks un ela ed o echnology.
JEL classi ica ion: C32, E32, E62.
Keywo ds: s uc u al ac o model, news shocks, in e ibili y, undamen alness.
∗Con ac : Dipa imen o di Economia Poli ica, ia Be enga io 51, 41100, Modena, I aly. Tel. +39
0592056851; e-mail: [email p o ec ed]
†The inancial suppo om he Spanish Minis y o Science and Inno a ion h ough g an ECO2009-
09847 and he Ba celona G adua e School Resea ch Ne wo k is g a e ully acknowledged. Con ac : O ice
B3.1130 Depa amen d’Economia i His o ia Economica, Edi ici B, Uni e si a Au onoma de Ba celona,
Bella e a 08193, Ba celona, Spain. Tel. +34 935814569; e-mail: [email p o ec ed]
‡Con ac : Uni e si ´a Bocconi, Via Roen gen 1, 20136, Milan, I aly. Tel. +39 0258363062; e-mail:
[email protected]
1
1 In oduc ion
In ecen yea s he e has been a enewed in e es in he idea ha business cycles could be
gene a ed by changes in expec a ions ( his idea da es back o Pigou, 1927). The li e a u e
has ocused on shocks ha ing delayed e ec s on echnology, he so-called ‘news shocks’.
The seminal pape by Beaud y and Po ie (2006) inds ha posi i e news shocks ha e a
posi i e impac on s ock p ices, consump ion, in es men and hou s wo ked and accoun
o mo e han hal o ou pu luc ua ions (see Figu e 10 in Beaud y and Po ie , 2006).1
These esul s do no squa e wi h s anda d neoclassical one-sec o models, in which good
news abou u u e echnology igge a weal h e ec ha a ec s posi i ely consump ion
bu nega i ely hou s, ou pu and in es men on impac . Beaud y and Po ie (2007),
Jaimo ich and Rebelo (2009), Schmi -G ohe and U ibe (2008) p opose models ha can
econcile he heo y wi h he abo e esul s.
Exis ing e idence has been ob ained by using small-scale VAR o VECM models.
This is p oblema ic, because when s uc u al shocks ha e delayed e ec s on mac oeco-
nomic a iables, VAR models using such a iables may be a ec ed by non- undamen alness
(Lippi and Reichlin, 1994, Leepe , Walke and Yang, 2008, Fo ni and Gambe i, 2010b,
Fe e, Ma he on and Sahuc, 2009). Non- undamen alness means ha he a iables used
by he econome ician do no con ain enough in o ma ion o eco e he s uc u al shocks
and he ela ed impulse esponse unc ions. The ques ion is essen ially whe he he s uc-
u al MA ep esen a ion o such a iables can be in e ed o no . I no , he a iables
do no ha e a VAR ep esen a ion in he s uc u al shocks, implying ha such shocks
canno be ob ained by es ima ing a VAR wi h hese a iables.2
To ge an in ui ion o he p oblem, assume ha he news shock a ec s o al ac o
p oduc i i y (TFP) wi h a one-pe iod delay. Clea ly, by obse ing TFP a ime we ge
in o ma ion abou news a i ed in −1, bu do no lea n any hing abou he cu en
shock. Coupling TFP wi h a se ies a ec ed by he shock on impac (like s ock p ices)
does no necessa ily sol e he p oblem, as shown in Sec ion 2.
In his pape we p esen new e idence on he e ec s o news shocks by es ima ing a
la ge-dimensional ac o model wi h US qua e ly da a. La ge ac o models, including
Fac o Augmen ed VARs (FAVARs), can be used o s uc u al economic analysis jus
like VAR models, as in Giannone, Reichlin and Sala (2004), Be nanke, Boi in and Eliasz
(2005), S ock and Wa son (2005), Fo ni, Giannone, Lippi and Reichlin (2009), Fo ni
1Beaud y and Lucke (2009) and Beaud y, Po ie and Dupaigne (2008) con i m he same empi ical
indings.
2A pa ial lis o e e ences on non- undamen alness includes Lippi and Reichlin (1993), Hansen and
Sa gen (1991), Cha i, Kehoe and McG a an (2005), Fe nandez-Villa e de, Rubio-Rami ez, Sa gen and
Wa son (2005), Giannone, Reichlin and Sala (2006).
2
and Gambe i (2010a).3Thei ad an age in he p esen con ex is ha hey a e no
a ec ed by he non- undamen alness p oblem, as shown in Fo ni, Giannone, Lippi and
Reichlin (2009).4The in ui ion is ha la ge ac o models, unlike VARs, include a la ge
amoun o in o ma ion ( i ually all a ailable mac oeconomic se ies), so ha insu icien
in o ma ion is unlikely. As a ma e o ac , ac o models ha e been success ul in
explaining well known VAR puzzles like he ‘p ice’ puzzle and he ‘exchange a e’ puzzle
(Be nanke, Boi in and Eliasz, 2005, Fo ni and Gambe i, 2010a). In addi ion, he ac o
model enables us o e i y whe he a gi en VAR in o ma ion se is a ec ed by non-
undamen alness o no . Ou es ing p ocedu e is explained in Sec ion 3.5.
Ou esul s a e he ollowing.
Fi s , we es ima e a wo-shock ac o model and apply he abo e es o he wo
a iables in he benchma k model o Beaud y and Po ie (TFP and s ock p ices). We
ind ha he s uc u al MA ep esen a ion o TFP and s ock p ices is non- undamen al.
Then we iden i y he news shock as in Beaud y and Po ie (2006), by assuming a ze o
impac e ec on TFP and ind ha he impulse esponses and a iance decomposi ions
ob ained wi h he ac o model a e comple ely di e en om hose ob ained by imposing
he same iden i ica ion scheme o a bi a ia e VECM. In pa icula , he e ec s on s ock
p ices a e much smalle .
Then we ocus on ou p e e ed ac o model speci ica ion (a six-shock speci ica ion).
We iden i y he news shock by imposing bo h a ze o impac e ec and a maximal long-
un e ec on TFP. The la e condi ion co esponds o he idea ha news shocks should
explain he main bulk o echnology in he long- un. We ind ha : (i) hou s wo ked,
in es men and ou pu ha e nega i e impac esponses, whe eas consump ion and s ock
p ices a e essen ially una ec ed on impac ; (ii) in es men , consump ion, ou pu and
s ock p ices inc ease g adually as TFP inc eases; (iii) news shocks accoun o abou
20-25% o business-cycle luc ua ions in in es men , consump ion and GDP. Such e ec s
a e essen ially in line wi h wha p edic ed by a s anda d neoclassical model.
Finally, we iden i y a s anda d echnology shock, ha ing non-ze o impac e ec on
p oduc i i y, by imposing ha no o he shock a ec s TFP con empo aneously. We ind
ha he news and he echnology shocks explain oge he almos all o TFP ola ili y a
all equencies, bu only 25-35% o business-cycle luc ua ions in in es men , consump-
ion and GDP, lea ing subs an ial oom o sou ces o ola ili y un ela ed o echnology.
3La ge ‘gene alized’ o ‘app oxima e’ dynamic ac o models a e speci ically designed o handle a la ge
amoun o in o ma ion. Ea ly e e ences a e Fo ni and Reichlin (1998), Fo ni, Hallin, Lippi and Reichlin
(2000), Fo ni and Lippi (2001), S ock and Wa son (2002a, 2002b), Bai and Ng (2002).
4This esul holds ue p o ided ha economic agen s can see he s uc u al shocks, as assumed in
mos o he cu en heo e ical li e a u e. A ecen no iceable excep ion is Lo enzoni (2009), whe e
agen s can only obse e echnology ‘news’ dis u bed by an agg ega e ‘noise’. We a e no conce ned wi h
his in e es ing case in he p esen pape .
3
O e all, ou esul s a e ai ly simila o hose ob ained by Ba sky and Sims (2009)
wi h a six- a iable VAR including in la ion, a sho e m in e es a e, consump ion and
a consume sen imen index, in addi ion o TFP and s ock p ices. Consis en ly wi h
his, ou es is no able o ejec undamen alness o such a iables.
The pape is s uc u ed as ollows. In Sec ion 2 we p o ide a simple analy ical exam-
ple ha shows how non- undamen alness can a ise in he p esence o news shocks. In Sec-
ion 3 we p esen he ac o model, a gue why i is no subjec o he non- undamen alness
p oblem, and desc ibe ou undamen alness es . Sec ion 4 p esen s empi ical esul s.
Sec ion 5 concludes.
2 Non- undamen alness and News Shocks
In his Sec ion we p esen a simple example, in which non- undamen alness appea s as
a consequence o he p esence o news shocks. Measu ed TFP, θ , is assumed o ollow
he non-s a iona y p ocess:
θ =θ −1+ε −2+u (1)
whe e ε is he news shock and u is he ‘s anda d’ echnology shock, a ec ing TFP on
impac . Agen s obse e he shock ε a ime and eac o i immedia ely, while he
shock will a ec TFP only a ime + 2. The e o e he econome ician will no be able
o iden i y ε by obse ing θ .
The ep esen a i e consume maximizes
E
∞
X
=0
β C ,
whe e C is consump ion and βis a discoun ac o , subjec o he cons ain
C +P S +1 = (P +θ )S ,
whe e P is he p ice o a sha e, S is he numbe o sha es and (P +θ )S is he o al
amoun o esou ces a ailable a ime . The equilib ium alue o asse p ices is gi en
by:
P =E
∞
X
j=1
βjθ +j
Conside ing (1), he abo e equa ion can be sol ed o ge he ollowing s uc u al MA
ep esen a ion
∆θ
∆P != L21
β2
1−β+βL β
1−β! ε
u !.(2)
4
The de e minan is
−β2
1−β−βz +β
1−βz2
which anishes o z= 1 and z=−β. As β < 1, he mo ing a e age is non in e ible and
he wo shocks u and ε a e non- undamen al o he a iables ∆P and ∆θ . No e en
a e y o wa d-looking a iable like s ock p ices con eys enough in o ma ion o eco e
he shock.
3 The s uc u al ac o model
In his pape we use he ac o model p esen ed in Fo ni, Giannone, Lippi and Reichlin
(2009, FGLR hence o h).5He e we p o ide a sho p esen a ion o he model, discuss
he ela ion wi h non- undamen alness and explain ou undamen alness es .
3.1 Rep esen a ion
We assume ha each mac oeconomic a iable xi is he sum o wo mu ually o hogonal
unobse able componen s, he common componen χi and he idiosync a ic componen
ξi :
xi =χi +ξi .(3)
The idiosync a ic componen s a e poo ly co ela ed in he c oss-sec ional dimension.6
They a ise om shocks o sou ces o a ia ion which conside ably a ec only a single
a iable o a small g oup o a iables. Fo a iables ela ed o pa icula sec o s, like
indus ial p oduc ion indexes o p oduc ion p ices, he idiosync a ic componen may
e lec sec o speci ic a ia ions; o s ic ly mac oeconomic a iables, like GDP, in es -
men o consump ion, he idiosync a ic componen can be in e p e ed as a measu emen
e o .7
The common componen s accoun o he bulk o he co-mo emen s be ween mac oe-
conomic a iables, being linea combina ions o a ela i ely small numbe o ac o s
5FGLR is a special case o he gene alized dynamic ac o model p oposed by Fo ni, e al. (2000,
2004, 2005) and Fo ni and Lippi (2001, 2010). This model di e s om he adi ional dynamic ac o
model o Sa gen and Sims (1977) and Geweke (1977) in ha he numbe o c oss-sec ional a iables is
in ini e and he idiosync a ic componen s a e allowed o be mu ually co ela ed o some ex en , along
he lines o Chambe lain (1983), Chambe lain and Ro hschild (1983) and Conno and Ko ajczyk (1988).
Closely ela ed models ha e been s udied by Fo ni and Reichlin (1998), S ock and Wa son (2002a, 2002b,
2005), Bai and Ng (2002, 2007), Bai (2003) and Be nanke e al. (2005).
6See FGLR, Assump ion 5 o a p ecise s a emen .
7Al ug, (1989), Sa gen , (1989), and I eland (2004) show ha he model can be in e p e ed as he
linea solu ion o a DSGE model wi h measu emen e o .
5

1 , 2 ,· · · , , no depending on i:
χi =a1i 1 +a2i 2 +· · · +a i =ai .(4)
The dynamic ela ions be ween he mac oeconomic a iables a ise om he ac ha
he ec o ollows he ela ion
=N(L)u ,(5)
whe e N(L) is a ×qma ix o a ional unc ions in he lag ope a o Land u =
(u1 u2 · · · uq )0is a q-dimensional ec o o o hono mal whi e noises, wi h q≤ .
Such whi e noises a e he s uc u al mac oeconomic shocks.8
The discussion in Sec ion 3.4 mo i a es he assump ion ha N(z) is ze oless, i.e.
ank(N(z)) = q o any z, which implies undamen alness. This ensu es ha has he
ini e o de VAR ep esen a ion (Ande son and Deis le , 2008)
D(L) = =Ru ,(6)
whe e D(L) is a × ma ix o polynomials such ha D(L)−1R=N(L) and R=N(0).
Combining equa ions (3) o (6), he model can be w i en in dynamic o m
xi =bi(L)u +ξi ,(7)
whe e
bi(L) = aiD(L)−1R. (8)
The en ies o he q-dimensional ec o bi(L) a e he impulse esponse unc ions.
3.2 Iden i ica ion
Rep esen a ion (7) is no unique, since he impulse esponse unc ions and he ela ed
p imi i e shocks a e no iden i ied. In pa icula , i His any o hogonal q×qma ix,
hen
χi =ci(L)
whe e ci(L) = bi(L)H0and =Hu . Howe e , assuming mu ually o hogonal s uc u al
shocks, pos -mul iplica ion by H0is he only admissible ans o ma ion, i.e. he impulse
esponse unc ions a e unique up o o hogonal ans o ma ions, jus like in s uc u al
VAR models (FGLR, P oposi ion 2).
8In he la ge dynamic ac o model li e a u e hey a e some imes called he “common” o “p imi i e”
shocks o “dynamic ac o s” (whe eas he en ies o a e he “s a ic ac o s”). Equa ions (3) o (5)
need u he quali ica ion o ensu e ha all o he ac o s a e loaded, so o speak, by enough a iables
wi h la ge enough loadings (see FGLR, Assump ion 4); his “pe asi eness” condi ion is necessa y o
ha e uniqueness o he common and he idiosync a ic componen s, as well as he numbe o s a ic ac o s
and dynamic ac o s q.
6
As a consequence, s uc u al analysis in ac o models can be ca ied on along lines
e y simila o hose o s anda d s uc u al VAR analysis. Speci ically q(q−1)/2 e-
s ic ions ha e o be imposed on he ma ix o impulse esponse unc ions Bn(L) =
(b1(L)0b2(L)0· · · bn(L)0)0, wi h n he numbe o a iables, o pin down all he elemen s o
H.
I he esea che is in e es ed in iden i ying jus a single shock, he a ge is o
de e mine he en ies o a single column o he ma ix H, say H1, which is enough o
ob ain he i s column o Bn(L), say Bn1(L).
3.3 Es ima ion
Es ima ion p oceeds h ough he ollowing s eps.
1. S a ing wi h an es ima e ˆ , he s a ic ac o s a e es ima ed by means o he i s
ˆ p incipal componen s o he a iables in he da ase , and he ac o loadings by
means o he associa ed eigen ec o s. P ecisely, le ˆ
Γxbe he sample a iance-
co a iance ma ix o he da a: he es ima ed loading ma ix ˆ
An= (ˆa0
1ˆa0
2· · · ˆa0
n)0
is he n× ma ix ha ing on he columns he no malized eigen ec o s co e-
sponding o he i s la ges ˆ eigen alues o ˆ
Γx, and he es ima ed ac o s a e
ˆ
=ˆ
A0
n(x1 x2 · · · xn )0.9
2. ˆ
D(L) and ˆ a e ob ained by unning a VAR(ˆp) wi h ˆ
whe e he numbe o lags
ˆpis chosen acco ding o some c i e ion.
3. Le ˆ
Γbe he sample a iance-co a iance ma ix o ˆ . Ha ing an es ima e ˆqo
he numbe o dynamic ac o s, an es ima e o a non-s uc u al ep esen a ion o
he common componen s is ob ained by using he spec al decomposi ion o ˆ
Γ.
P ecisely, le ˆµ
j,j= 1,...,ˆq, be he j- h eigen alue o ˆ
Γ, in dec easing o de , ˆ
M
he q×qdiagonal ma ix wi h qˆµ
jas i s (j, j) en y, and ˆ
K he ×qma ix wi h
he co esponding no malized eigen ec o s on he columns. The es ima ed ma ix
o non-s uc u al impulse esponse unc ions is
ˆ
Cn(L) = ˆ
Anˆ
D(L)−1ˆ
Kˆ
M.(9)
To accoun o es ima ion unce ain y, he ollowing non-o e lapping block boo s ap
echnique is adop ed. Le X= [xi ] be he T×nma ix o da a. Such ma ix is pa -
i ioned in o Ssub-ma ices Xs(blocks), s= 1, . . . , S, o dimension τ×n,τbeing he
9The ac o s a e iden i ied only up o linea ans o ma ions. Wha is es ima ed is a basis o he
ac o space.
7
in ege pa o T/S.10 An in ege hsbe ween 1 and Sis d awn andomly wi h ein o-
duc ion S imes o ob ain he sequence h1, . . . , hS. A new a i icial sample o dimension
τS ×nis hen gene a ed as X∗=hX0
h1X0
h2· · · X0
hSi0and he co esponding impulse
esponse unc ions, ˆ
Cn(L), a e es ima ed and he iden i ying assump ions a e imposed
o ge H1and he co esponding impulse esponse unc ions ˆ
Bn1(L) = ˆ
Cn(L)H1. A se
o s uc u al impulse esponse unc ions is ob ained by epea ing d awing, es ima ion
and iden i ica ion. Con idence bands a e ob ained by aking he ele an pe cen iles o
he poin -wise dis ibu ions.
3.4 Tall sys ems and undamen alness
He e we discuss why he assump ion o undamen alness is jus i ied in he ac o model.
Le us go back o equa ion (5)
=N(L)u ,
whe e N(L) is a ( ×q) ma ix o a ional unc ions in he lag ope a o L, wi h ≥q.
Unde wha condi ions a e he shocks u undamen al o ? A necessa y and su icien
condi ion is ha he ank o N(z) be q o all zsuch ha |z|<1 (see e.g. Rozano ,
1967, Ch. 1, Sec ion 10, and Ch. 2, p. 76).
Le us i s ocus on he pa icula case =q, and in e p e as a ec o o obse able
a iables o be used in a VAR. The abo e undamen alness condi ion educes o he
equi emen ha he de e minan o N(z) does no anish wi hin he uni ci cle in he
complex plane. I his condi ion holds, hen he shock u can be ound using a VAR
o . In gene al, howe e , he e is no gua an ee ha he condi ion holds, as shown in
Sec ion 2.
Now le us u n o he case > q, which is he no mal case in he ac o model. In
such case N(z) is a “ all”, ec angula ma ix. I s ank is less han q o some z, i.e. he
shock is non- undamen al, only i all o he (q×q) sub-ma ices o N(z) a e singula .
Clea ly his is a e y special case, since i equi es
q!−1 equali ies o be sa is ied.
The e o e, in gene al, when > q,N(z) has ank q o all zand he ep esen a ion can
be assumed undamen al.
As a e y elemen a y example, conside he case q= 1, = 2, 1 =u + 2u −1,
2 = 2u −1. He e u is non- undamen al o bo h 1 and 2 , and canno be ound
as a linea combina ion o p esen and pas alues o a single ac o . Howe e , u is
undamen al o he ec o , since u = 1 − 2 .
Obse e ha undamen alness o ep esen a ion (5) implies undamen alness o he
10No e ha τhas o be la ge enough o e ain ele an lagged au o- and c oss-co a iances.
8
sys em
χ =Bn(L)u ,
whe e χ = (χ1 · · · χn )0and Bn(L) = AnD(L)−1R,An= (a0
1a0
2· · · a0
n)0(p o ided ha
Anhas ull column ank).
3.5 Tes ing o undamen alness
While he whole sys em Bn(L) is undamen al, he q-dimensional squa e subma ices
o Bn(L) co esponding o selec ed subse s o a iables can be singula o alues o z
wi hin he uni ci cle (wi hou hu ing consis ency o es ima ion). P ecisely, conside ing
aq-dimensional ec o o in ege s I, wi h elemen s Ii,i= 1, . . . , q,u is undamen al
o he sub ec o χI = (χI1 · · · χIq )0=BI(L)u i de BI(z) does no anish wi hin he
uni ci cle.
A es o undamen alness o a pa icula squa e subsys em can hen be pe o med by
looking a he es ima ed dis ibu ion o he modulus ρo he smalles oo . We ejec he
null o undamen alness (ρ≥1) agains he al e na i e o non- undamen alness (ρ < 1)
a he signi icance le el αas long as he equency o alues la ge han 1 is smalle han
α.
Rejec ion o undamen alness implies ha an hypo he ical VAR model using χI
would be misspeci ied. In p inciple, such an implica ion canno be di ec ly ex ended o
he ue VAR se ing, whe e xI is used in place o χI . In p ac ice howe e he idiosyn-
c a ic componen s a e usually e y small, so ha ejec ion (accep ance) o undamen al-
ness p o ides a use ul indica ion agains (in a o o ) a pa icula VAR speci ica ion.
4 Empi ics
4.1 Da a and model speci ica ion
Ou da a se is composed o 116 US qua e ly se ies, co e ing he pe iod 1959-I o 2007-
IV. Mos se ies a e aken om he FRED da abase. A ew s ock ma ke and leading
indica o s a e aken om Da as eam. Some se ies ha e been cons uc ed by ou sel es as
ans o ma ions o he o iginal FRED se ies. The se ies include bo h na ional accoun -
ing da a like GDP, in es men , consump ion and he GDP de la o , TFP and consume s
sen imen which a e a ailable only a qua e ly equency, and se ies like indus ial p o-
duc ion indices, CPI, PPI and employmen , which a e p oduced mon hly. Mon hly da a
ha e been empo ally agg ega ed o ge qua e ly igu es.
As equi ed by he model, he da a a e ans o med o ob ain s a iona i y. Following
S ock and Wa son (2005), p ices and nominal a iables a e aken in second di e ences o
logs, a he han in i s di e ences o logs, and in e es a es in i s di e ences, a he
9
no.se ies T ans . Mnemonic Long Label
46 2 AWOTMAN A e age Weekly Hou s: O e ime: Manu ac u ing
47 2 CIVPART Ci ilian Pa icipa ion Ra e
48 5 CLF16OV Ci ilian Labo Fo ce
49 5 CE16OV Ci ilian Employmen
50 5 USPRIV All Employees: To al P i a e Indus ies
51 5 USGOOD All Employees: Goods-P oducing Indus ies
52 5 SRVPRD All Employees: Se ice-P o iding Indus ies
53 5 UNEMPLOY Unemployed
54 5 UEMPMEAN A e age (Mean) Du a ion o Unemploymen
55 2 UNRATE Ci ilian Unemploymen Ra e
56 5 HOUST Housing S a s: To al: New P i a ely Owned Housing Uni s S a ed
57 2 FEDFUNDS E ec i e Fede al Funds Ra e
58 2 TB3MS 3-Mon h T easu y Bill: Seconda y Ma ke Ra e
59 2 GS1 1-Yea T easu y Cons an Ma u i y Ra e
60 2 GS10 10-Yea T easu y Cons an Ma u i y Ra e
61 2 AAA Moody’s Seasoned Aaa Co po a e Bond Yield
62 2 BAA Moody’s Seasoned Baa Co po a e Bond Yield
63 2 MPRIME Bank P ime Loan Ra e
64 6 BOGNONBR Non-Bo owed Rese es o Deposi o y Ins i u ions
65 6 TRARR Boa d o Go e no s To al Rese es, Adjus ed o Changes in Rese e
66 6 BOGAMBSL Boa d o Go e no s Mone a y Base, Adjus ed o Changes in Rese e
67 6 M1SL M1 Money S ock
68 6 M2MSL M2 Minus
69 6 M2SL M2 Money S ock
70 6 BUSLOANS Comme cial and Indus ial Loans a All Comme cial Banks
71 6 CONSUMER Consume (Indi idual) Loans a All Comme cial Banks
72 6 LOANINV To al Loans and In es men s a All Comme cial Banks
73 6 REALLN Real Es a e Loans a All Comme cial Banks
74 6 TOTALSL To al Consume C edi Ou s anding
75 6 CPIAUCSL Consume P ice Index Fo All U ban Consume s: All I ems
76 6 CPIULFSL Consume P ice Index o All U ban Consume s: All I ems Less Food
77 6 CPILEGSL Consume P ice Index o All U ban Consume s: All I ems Less Ene gy
78 6 CPILFESL Consume P ice Index o All U ban Consume s: All I ems Less Food & Ene gy
79 6 CPIENGSL Consume P ice Index o All U ban Consume s: Ene gy
80 6 CPIUFDSL Consume P ice Index o All U ban Consume s: Food
81 6 PPICPE P oduce P ice Index Finished Goods: Capi al Equipmen
82 6 PPICRM P oduce P ice Index: C ude Ma e ials o Fu he P ocessing
83 6 PPIFCG P oduce P ice Index: Finished Consume Goods
84 6 PPIFGS P oduce P ice Index: Finished Goods
85 6 OILPRICE Spo Oil P ice: Wes Texas In e media e
86 5 USSHRPRCF US Dow Jones Indus ials Sha e P ice Index (EP) NADJ
87 5 US500STK US S anda d & Poo ’s Index i 500 Common S ocks
88 5 USI62...F US Sha e P ice Index NADJ
89 5 USNOIDN.D US Manu ac u e s New O de s o Non De ense Capi al Goods (BCI 27)
90 5 USCNORCGD US New O de s o Consume Goods & Ma e ials (BCI 8) CONA
16

no.se ies T ans . Mnemonic Long Label
91 1 USNAPMNO US ISM Manu ac u e s Su ey: New O de s Index SADJ
92 5 USVACTOTO US Index o Help Wan ed Ad e ising VOLA
93 5 USCYLEAD US The Con e ence Boa d Leading Economic Indica o s Index SADJ
94 5 USECRIWLH US Economic Cycle Resea ch Ins i u e Weekly Leading Index
95 2 GS10-FEDFUNDS
96 2 GS1-FEDFUNDS
97 2 BAA-FEDFUNDS
98 5 GEXPND/GDPDEF Go e nmen Cu en Expendi u es/ GDP de la o
99 5 GRECPT/GDPDEF Go e nmen Cu en Receip s/ GDP de la o
100 2 GDEF Go e nnen Real Expend-Real Receip s
101 5 GCEC1 Real Go e nmen Consump ion Expendi u es & G oss In es men , 1 Decimal
102 1 Fe nald’s TFP g ow h CU adjus ed
103 1 Fe nald’s TFP g ow h
104 5 DOW JOONES/GDP DEFL
105 5 S&P500/GDP DEFL
106 1 Fe nald’s TFP g ow h - In es men
107 1 Fe nald’s TFP g ow h - Consump ion
108 1 Fe nald’s TFP g ow h CU - In es men
109 1 Fe nald’s TFP g ow h CU - Consump ion
110 1 Pe sonal Finance Cu en
111 1 Pe sonal Finance Expec ed
112 1 Business Condi ion 12 Mon hs
113 1 Business Condi ion 5 Yea s
114 1 Buying Condi ions
115 1 Consume ’s sen imen : Cu en Index
116 1 Consume ’s sen imen : Expec ed Index
17
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21

Tables
jVa iables (Ij)
Two Shocks
1 TFP (102) S ock P (105)
2 TFP (103) S ock P (105)
Six Shocks
1 TFP (102) S ock P (105) Non Du . C (12) In . (7) Hou s (27) GDP (1)
2 TFP (103) S ock P (105) Non Du . C (12) In . (7) Hou s (27) GDP (1)
3 TFP (102) S ock P (105) Non Du . C (12) GDP (1) CPI (75) 3M T-Bill (58)
4 TFP (102) S ock P (105) Non Du . C (12) Hou s (27) CPI (75) 3M T-Bill (58)
5 TFP (102) S ock P (105) Non Du . C (12) Sen imen (116) CPI (75) 3M T-Bill (58)
Table 1: Subse s o a iables (I) used in he es desc ibed in Sec ion 3.5. The numbe s
in b acke s co espond o hose in he Appendix.
jMean Median 68% 90% 95% Poin es .
Two Shocks
1 0.531 0.515 0.768 1.060 1.086 0.481
2 0.711 0.812 0.940 1.102 1.128 0.861
Six Shocks
1 0.692 0.763 0.934 1.084 1.125 0.459
2 0.636 0.665 0.878 1.023 1.066 0.279
3 0.645 0.666 0.835 1.051 1.083 0.755
4 0.557 0.546 0.712 0.966 1.041 0.294
5 0.856 0.952 1.072 1.161 1.192 1.099
Table 2: Moduli o he smalles oo o he subma ices BI(L) de ined in Table 1.
22
Va iables Ho izons
0 4 8 40
Fac o model
TFP (102) 0.0 6.5 7.2 7.4
S ock P ices (105) 16.1 55.2 61.2 63.4
VECM
TFP (102) 0 0.7 0.6 33.9
S ock P ices (105) 99.7 97.6 96.5 93.4
Table 3: Explained o ecas e o a iance (pe cen ages) a a ious ho izons in he
wo-shock ac o model and he bi a ia e VAR o he common componen s using he
Cholesky iden i ica ion (le els). The numbe s in b acke s co espond o hose in he
Appendix.
23
Va iables Ho izons (a) % To al Va iance % Va iance 2-8 Yea s
0 4 8 40 (b) (c)
News shock
TFP (102) 0.0 11.1 17.6 29.9 7.8 14.6
GDP (1) 6.2 11.0 11.3 15.3 15.2 19.9
Consump ion (11) 2.9 17.0 27.0 40.2 25.0 25.0
In es men (7) 8.0 14.1 12.3 12.5 20.0 20.3
Hou s (27) 26.1 14.4 17.5 15.7 21.9 19.9
S ock P ices (105) 6.9 7.0 8.1 9.8 10.0 10.1
Sen imen cu en (115) 7.0 14.7 21.5 24.4 24.4 22.1
Sen imen expec ed (116) 26.4 31.1 36.0 37.9 37.9 32.3
P ices (75) 19.5 23.9 20.6 15.5 23.7 27.1
3M T-Bill (58) 28.5 25.5 20.5 18.4 25.4 25.6
Technology shock
TFP (102) 100.0 85.7 79.2 69.2 80.4 76.3
GDP (1) 64.2 22.4 20.8 23.6 38.1 12.0
Consump ion (11) 30.7 16.2 16.0 16.5 17.8 9.2
In es men (7) 8.7 2.6 2.4 3.8 6.1 2.9
Hou s (27) 0.7 0.9 1.0 0.9 2.2 1.6
S ock P ices (105) 0.5 0.8 1.0 1.3 2.7 1.4
Sen imen cu en (115) 3.3 3.0 3.6 4.0 4.0 3.0
Sen imen expec ed (116) 13.8 8.0 7.8 7.9 7.9 6.1
P ices (75) 1.0 1.1 1.4 1.6 2.9 1.4
3M T-Bill (58) 2.6 1.5 1.4 1.3 4.0 1.7
Table 4: Va iance decomposi ion. (a) F ac ion o he a iance o he o ecas e o o
he le els o he a iables a di e en ho izon (b) Pe cen age o a iance o he a iables
ans o med o ge s a iona i y explained by he shock (c) Pe cen age o cyclical a iance
(o pe iodici y be ween 2 o 8 yea s) explained by he shock. The numbe s in b acke s
co espond o hose in he Appendix.
24
Figu es
Figu e 1: Impulse esponse unc ions in he wo-shocks model. Le column: echnology
shock, igh column: news shock. Uppe ow: esponse o TFP; Lowe ow: sponses o
s ock p ices. Solid: ac o model (median). Do ed: ac o model 68% con idence bands.
Dashed: VECM o he common componen s.
25